Toward axial accuracy prediction and optimization of metal tube bending forming: A novel GRU-integrated Pb-NSGA-III optimization framework

characteristics of metal materials. It is difficult to obtain the accurate axial springback information before bending forming, not to mention formulating a reasonable processing plan to compensate springback. To alleviate this difficulty, a novel optimization framework is constructed which takes the radius changes series (RCS) as the new axial accuracy evaluation index for the first time. The optimization framework contains a GRU-based deep learning network as the prediction module to predict the springback more reliably. Subsequently, NSGA-III has been improved by the proposed guiding factor (GF) and dynamic reference points (DRF) algorithm, i.e, priority-based NSGA-III (Pb-NSGA-III), which can more efficiently deal with the objectives with different priorities when generating the processing plan. With the help of the finite element (FE) and bending experiment, the springback dataset construction and the accuracy verification can be achieved. The results show that the framework can achieve high-precision and robust prediction of the tube axial accuracy. Compared with the existing commonly used multi-objective algorithms, the proposed Pb-NSGA-III shows its superiority in engineering application.